Adaptive beam sweeping for 5G or other next generation network

ABSTRACT

Given real-time user equipment (UE) measurements from an open radio access network (O-RAN) infrastructure, a radio access network intelligent controller (RIC) can compute a UE distribution context space. The O-RAN can comprise gNode-Bs, centralized units, and distributed units. The UE distribution context space computations can be performed by a UE context correlator module of the RIC. The UE context correlator module can also utilize a pre-defined UE context model, which contains definitions and values for various UE context attributes to generate adaptive beam-forming patterns.

TECHNICAL FIELD

This disclosure relates generally to facilitating adaptive beamsweeping. For example, this disclosure relates to facilitating adaptivebeam sweeping for a 5G, or other next generation network, air interface.

BACKGROUND

5th generation (5G) wireless systems represent a next major phase ofmobile telecommunications standards beyond the currenttelecommunications standards of 4^(th) generation (4G). Rather thanfaster peak Internet connection speeds, 5G planning aims at highercapacity than current 4G, allowing a higher number of mobile broadbandusers per area unit, and allowing consumption of higher or unlimiteddata quantities. This would enable a large portion of the population tostream high-definition media many hours per day with their mobiledevices, when out of reach of wireless fidelity hotspots. 5G researchand development also aims at improved support of machine-to-machinecommunication, also known as the Internet of things, aiming at lowercost, lower battery consumption, and lower latency than 4G equipment.

The above-described background relating to adaptive beam sweeping ismerely intended to provide a contextual overview of some current issues,and is not intended to be exhaustive. Other contextual information maybecome further apparent upon review of the following detaileddescription.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive embodiments of the subject disclosureare described with reference to the following figures, wherein likereference numerals refer to like parts throughout the various viewsunless otherwise specified.

FIG. 1 illustrates an example wireless communication system in which anetwork node device (e.g., network node) and user equipment (UE) canimplement various aspects and embodiments of the subject disclosure.

FIG. 2 illustrates an example schematic system block diagram of radioaccess network intelligent controller for adaptive beam sweepingaccording to one or more embodiments.

FIG. 3 illustrates an example schematic system block diagram of UEdistributed model as a multi-dimensional context space for adaptive beamsweeping according to one or more embodiments.

FIG. 4 illustrates an example schematic system comprising a blockdiagram of radio access network intelligent controller and an externalmachine-learning component for adaptive beam sweeping according to oneor more embodiments.

FIG. 5 illustrates an example schematic system comprising a blockdiagram of radio access network intelligent controller and an open radioaccess network (O-RAN) for adaptive beam sweeping according to one ormore embodiments.

FIG. 6 illustrates an example flow diagram for a method for adaptivebeam sweeping for a 5G network according to one or more embodiments.

FIG. 7 illustrates an example flow diagram for a system for adaptivebeam sweeping for a 5G network according to one or more embodiments.

FIG. 8 illustrates an example flow diagram for a machine-readable mediumfor adaptive beam sweeping for a 5G network according to one or moreembodiments.

FIG. 9 illustrates an example block diagram of an example mobile handsetoperable to engage in a system architecture that facilitates securewireless communication according to one or more embodiments describedherein.

FIG. 10 illustrates an example block diagram of an example computeroperable to engage in a system architecture that facilitates securewireless communication according to one or more embodiments describedherein.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forth toprovide a thorough understanding of various embodiments. One skilled inthe relevant art will recognize, however, that the techniques describedherein can be practiced without one or more of the specific details, orwith other methods, components, materials, etc. In other instances,well-known structures, materials, or operations are not shown ordescribed in detail to avoid obscuring certain aspects.

Reference throughout this specification to “one embodiment,” or “anembodiment,” means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment. Thus, the appearances of the phrase “in oneembodiment,” “in one aspect,” or “in an embodiment,” in various placesthroughout this specification are not necessarily all referring to thesame embodiment. Furthermore, the particular features, structures, orcharacteristics may be combined in any suitable manner in one or moreembodiments.

As utilized herein, terms “component,” “system,” “interface,” and thelike are intended to refer to a computer-related entity, hardware,software (e.g., in execution), and/or firmware. For example, a componentcan be a processor, a process running on a processor, an object, anexecutable, a program, a storage device, and/or a computer. By way ofillustration, an application running on a server and the server can be acomponent. One or more components can reside within a process, and acomponent can be localized on one computer and/or distributed betweentwo or more computers.

Further, these components can execute from various machine-readablemedia having various data structures stored thereon. The components cancommunicate via local and/or remote processes such as in accordance witha signal having one or more data packets (e.g., data from one componentinteracting with another component in a local system, distributedsystem, and/or across a network, e.g., the Internet, a local areanetwork, a wide area network, etc. with other systems via the signal).

As another example, a component can be an apparatus with specificfunctionality provided by mechanical parts operated by electric orelectronic circuitry; the electric or electronic circuitry can beoperated by a software application or a firmware application executed byone or more processors; the one or more processors can be internal orexternal to the apparatus and can execute at least a part of thesoftware or firmware application. As yet another example, a componentcan be an apparatus that provides specific functionality throughelectronic components without mechanical parts; the electroniccomponents can include one or more processors therein to executesoftware and/or firmware that confer(s), at least in part, thefunctionality of the electronic components. In an aspect, a componentcan emulate an electronic component via a virtual machine, e.g., withina cloud computing system.

The words “exemplary” and/or “demonstrative” are used herein to meanserving as an example, instance, or illustration. For the avoidance ofdoubt, the subject matter disclosed herein is not limited by suchexamples. In addition, any aspect or design described herein as“exemplary” and/or “demonstrative” is not necessarily to be construed aspreferred or advantageous over other aspects or designs, nor is it meantto preclude equivalent exemplary structures and techniques known tothose of ordinary skill in the art. Furthermore, to the extent that theterms “includes,” “has,” “contains,” and other similar words are used ineither the detailed description or the claims, such terms are intendedto be inclusive—in a manner similar to the term “comprising” as an opentransition word—without precluding any additional or other elements.

As used herein, the term “infer” or “inference” refers generally to theprocess of reasoning about, or inferring states of, the system,environment, user, and/or intent from a set of observations as capturedvia events and/or data. Captured data and events can include user data,device data, environment data, data from sensors, sensor data,application data, implicit data, explicit data, etc. Inference can beemployed to identify a specific context or action, or can generate aprobability distribution over states of interest based on aconsideration of data and events, for example.

Inference can also refer to techniques employed for composinghigher-level events from a set of events and/or data. Such inferenceresults in the construction of new events or actions from a set ofobserved events and/or stored event data, whether the events arecorrelated in close temporal proximity, and whether the events and datacome from one or several event and data sources. Various classificationschemes and/or systems (e.g., support vector machines, neural networks,expert systems, Bayesian belief networks, fuzzy logic, and data fusionengines) can be employed in connection with performing automatic and/orinferred action in connection with the disclosed subject matter.

In addition, the disclosed subject matter can be implemented as amethod, apparatus, or article of manufacture using standard programmingand/or engineering techniques to produce software, firmware, hardware,or any combination thereof to control a computer to implement thedisclosed subject matter. The term “article of manufacture” as usedherein is intended to encompass a computer program accessible from anycomputer-readable device, machine-readable device, computer-readablecarrier, computer-readable media, or machine-readable media. Forexample, computer-readable media can include, but are not limited to, amagnetic storage device, e.g., hard disk; floppy disk; magneticstrip(s); an optical disk (e.g., compact disk (CD), a digital video disc(DVD), a Blu-ray Disc™ (BD)); a smart card; a flash memory device (e.g.,card, stick, key drive); and/or a virtual device that emulates a storagedevice and/or any of the above computer-readable media.

As an overview, various embodiments are described herein to facilitateadaptive beam sweeping for a 5G air interface or other next generationnetworks. For simplicity of explanation, the methods (or algorithms) aredepicted and described as a series of acts. It is to be understood andappreciated that the various embodiments are not limited by the actsillustrated and/or by the order of acts. For example, acts can occur invarious orders and/or concurrently, and with other acts not presented ordescribed herein. Furthermore, not all illustrated acts may be requiredto implement the methods. In addition, the methods could alternativelybe represented as a series of interrelated states via a state diagram orevents. Additionally, the methods described hereafter are capable ofbeing stored on an article of manufacture (e.g., a machine-readablestorage medium) to facilitate transporting and transferring suchmethodologies to computers. The term article of manufacture, as usedherein, is intended to encompass a computer program accessible from anycomputer-readable device, carrier, or media, including a non-transitorymachine-readable storage medium.

It should be noted that although various aspects and embodiments havebeen described herein in the context of 5G, Universal MobileTelecommunications System (UMTS), and/or Long Term Evolution (LTE), orother next generation networks, the disclosed aspects are not limited to5G, a UMTS implementation, and/or an LTE implementation as thetechniques can also be applied in 3G, 4G or LTE systems. For example,aspects or features of the disclosed embodiments can be exploited insubstantially any wireless communication technology. Such wirelesscommunication technologies can include UMTS, Code Division MultipleAccess (CDMA), Wi-Fi, Worldwide Interoperability for Microwave Access(WiMAX), General Packet Radio Service (GPRS), Enhanced GPRS, ThirdGeneration Partnership Project (3GPP), LTE, Third Generation PartnershipProject 2 (3GPP2) Ultra Mobile Broadband (UMB), High Speed Packet Access(HSPA), Evolved High Speed Packet Access (HSPA+), High-Speed DownlinkPacket Access (HSDPA), High-Speed Uplink Packet Access (HSUPA), Zigbee,or another IEEE 802.XX technology. Additionally, substantially allaspects disclosed herein can be exploited in legacy telecommunicationtechnologies.

Described herein are systems, methods, articles of manufacture, andother embodiments or implementations that can facilitate adaptive beamsweeping for a 5G network. Facilitating adaptive beam sweeping for a 5Gnetwork can be implemented in connection with any type of device with aconnection to the communications network (e.g., a mobile handset, acomputer, a handheld device, etc.) any Internet of things (TOT) device(e.g., toaster, coffee maker, blinds, music players, speakers, etc.),and/or any connected vehicles (cars, airplanes, space rockets, and/orother at least partially automated vehicles (e.g., drones)). In someembodiments the non-limiting term user equipment (UE) is used. It canrefer to any type of wireless device that communicates with a radionetwork node in a cellular or mobile communication system. Examples ofUE are target device, device to device (D2D) UE, machine type UE or UEcapable of machine to machine (M2M) communication, PDA, Tablet, mobileterminals, smart phone, laptop embedded equipped (LEE), laptop mountedequipment (LME), USB dongles etc. Note that the terms element, elementsand antenna ports can be interchangeably used but carry the same meaningin this disclosure. The embodiments are applicable to single carrier aswell as to multicarrier (MC) or carrier aggregation (CA) operation ofthe UE. The term carrier aggregation (CA) is also called (e.g.interchangeably called) “multi-carrier system”, “multi-cell operation”,“multi-carrier operation”, “multi-carrier” transmission and/orreception.

In some embodiments the non-limiting term radio network node or simplynetwork node is used. It can refer to any type of network node thatserves UE is connected to other network nodes or network elements or anyradio node from where UE receives a signal. Examples of radio networknodes are Node B, base station (BS), multi-standard radio (MSR) nodesuch as MSR BS, eNode B, network controller, radio network controller(RNC), base station controller (BSC), relay, donor node controllingrelay, base transceiver station (BTS), access point (AP), transmissionpoints, transmission nodes, RRU, RRH, nodes in distributed antennasystem (DAS) etc.

Cloud radio access networks (RAN) can enable the implementation ofconcepts such as software-defined network (SDN) and network functionvirtualization (NFV) in 5G networks. This disclosure can facilitate ageneric channel state information framework design for a 5G network.Certain embodiments of this disclosure can comprise an SDN controllerthat can control routing of traffic within the network and between thenetwork and traffic destinations. The SDN controller can be merged withthe 5G network architecture to enable service deliveries via openapplication programming interfaces (“APIs”) and move the network coretowards an all internet protocol (“IP”), cloud based, and softwaredriven telecommunications network. The SDN controller can work with, ortake the place of policy and charging rules function (“PCRF”) networkelements so that policies such as quality of service and trafficmanagement and routing can be synchronized and managed end to end.

To meet the huge demand for data centric applications, 4G standards canbe applied 5G, also called new radio (NR) access. 5G networks cancomprise the following: data rates of several tens of megabits persecond supported for tens of thousands of users; 1 gigabit per secondcan be offered simultaneously to tens of workers on the same officefloor; several hundreds of thousands of simultaneous connections can besupported for massive sensor deployments; spectral efficiency can beenhanced compared to 4G; improved coverage; enhanced signalingefficiency; and reduced latency compared to LTE. In multicarrier systemsuch as OFDM, each subcarrier can occupy bandwidth (e.g., subcarrierspacing). If the carriers use the same bandwidth spacing, then it can beconsidered a single numerology. However, if the carriers occupydifferent bandwidth and/or spacing, then it can be considered a multiplenumerology.

The current approach to 5G beam sweeping requires a radio unit (RU) tosend a synchronization signal (as a beam) in every angular direction forabout 5 millisecond with a 20 millisecond periodicity. Basically, the RUperforms a rather high frequency sweeping of its surrounding area. Thisprocedure is somewhat conservative and attempts not to leave any UEbehind. However, it fails to consider that UE density (e.g., # of UE'spresent at any point in time in a sector) varies by time of day, day ofweek, location, and context (e.g., special events, weather, etc.). Thus,the current approach is less efficient energy consumption wise.

This disclosure comprises methods and procedures to control 5G beamsweeping in a manner that is adaptive to both a detected distribution ofUEs and an expected distribution of UE's in the sector of an RU. Thedistribution of UE's can be modeled in a multi-dimensional “contextspace”. For example, the solution can have three dimensions (e.g., time,location, and event), or any arbitrary number of dimensions, along withpossible attributes of each of the three dimensions.

Given real-time UE measurements from an open radio access network(O-RAN) infrastructure, a radio access network intelligent controller(RIC) can compute the UE distribution context space. The O-RAN cancomprise gNBs, centralized units, and distributed units. Thiscomputation can be performed by a UE context correlator module of theRIC. The UE context correlator module can utilize a pre-defined UEcontext model module, which contains definitions and values for variousUE context attributes (e.g., definition of what constitutes a morningcommute hour, holidays, airport locations, game event timing andlocations, etc.).

The UE distribution model can be provided to a machine learning trainingmodule (e.g., an open network automation platform (ONAP), etc.) todevelop machine learning (ML) models of various context attributes basedon historical data (e.g., models of the numbers of expected UE's in anoffice building during a working day, etc.).

The real-time UE distribution model (based on detected, actual UEdistributions) and the ML (history) based prediction model can be usedby a UE distribution model estimator component of the RIC to estimatethe UE distribution model to be used next. The RIC beam sweeping controlestimator module can use the estimated UE distribution model tocalculate the optimal beam sweeping control parameters. These parameterscan then be provided by the RIC to the O-RAN infrastructure. Thus, thisprocess can facilitate efficient control of the beam sweeping process toincrease energy saving efficiency, optimally match the beam sweepingprocess with the demands of real-world external situation, eliminate theneed for sweeping every UE, reduce power, and achieve faster beamsweeping.

It should also be noted that an artificial intelligence (AI) componentcan facilitate automating one or more features in accordance with thedisclosed aspects. For purposes of this disclosure, ML and AI are usedinterchangeably. A memory and a processor as well as other componentscan include functionality with regard to the figures. The disclosedaspects in connection with adaptive beam sweeping can employ variousAI-based schemes for carrying out various aspects thereof. For example,a process for detecting one or more trigger events, modifying a beamsweeping pattern as a result of the one or more trigger events, andtransmitting the beams, and so forth, can be facilitated with an exampleautomatic classifier system and process. In another example, a processfor penalizing one beam while preferring another beam can be facilitatedwith the example automatic classifier system and process.

An example classifier can be a function that maps an input attributevector, x=(x1, x2, x3, x4, xn), to a confidence that the input belongsto a class, that is, f(x)=confidence(class). Such classification canemploy a probabilistic and/or statistical-based analysis (e.g.,factoring into the analysis utilities and costs) to prognose or infer anaction that can be automatically performed. In the case of communicationsystems, for example, attributes can be a signal strength and atechnology and the classes can be an output power reduction value. Inanother example, the attributes can be a signal strength, a technology,and static or dynamic scenarios that can affect output power.

A support vector machine (SVM) is an example of a classifier that can beemployed. The SVM can operate by finding a hypersurface in the space ofpossible inputs, which the hypersurface attempts to split the triggeringcriteria from the non-triggering events. Intuitively, this makes theclassification correct for testing data that is near, but not identicalto training data. Other directed and undirected model classificationapproaches include, for example, naïve Bayes, Bayesian networks,decision trees, neural networks, fuzzy logic models, and probabilisticclassification models providing different patterns of independence canbe employed. Classification as used herein also may be inclusive ofstatistical regression that is utilized to develop models of priority.

The disclosed aspects can employ classifiers that are explicitly trained(e.g., via a generic training data) as well as implicitly trained (e.g.,via observing mobile device usage as it relates to triggering events,observing network frequency/technology, receiving extrinsic information,and so on). For example, SVMs can be configured via a learning ortraining phase within a classifier constructor and feature selectionmodule. Thus, the classifier(s) can be used to automatically learn andperform a number of functions, including but not limited to allocatingbeams, modifying beam patterns, and so forth. The criteria can include,but is not limited to, predefined values, real-time values or historicalparameters, service provider preferences and/or policies, and so on.

In one embodiment, described herein is a method comprising modeling, bya wireless network device comprising a processor, a real-timedistribution of the mobile devices based on attribute datarepresentative of attributes of mobile devices, resulting in a real-timemodel. The method can comprise correlating, by the wireless networkdevice, the attribute data to defined parameters to assign a context tothe attributes. Based on a result of the correlating, the method cancomprise facilitating, by the wireless network device, generating amachine-learning model. Additionally, based on the machine-learningmodel, the method can comprise estimating, by the wireless networkdevice, a distribution model, resulting in an estimated distributionmodel used to determine a beam sweep process to be applied to the mobiledevices.

According to another embodiment, a system can facilitate, the modeling areal-time distribution of the mobile devices based on attribute datarepresentative of attributes of mobile devices, resulting in a real-timedistribution model. The system can comprise correlating the attributedata to defined parameters to assign a context to the attributes. Inresponse to the correlating and based on historical distribution data,the system can comprise generating a machine-learned distribution model.Based on the real-time distribution model and the machine-learneddistribution model, the system can comprise estimating a distributionmodel, resulting in an estimated distribution model applicable to themobile devices. Additionally, based on the estimated distribution model,the system can comprise determining a beam sweeping control parameter.

According to yet another embodiment, described herein is amachine-readable storage medium that can perform the operationscomprising generating a distribution model of the mobile devices,wherein the distribution model is an actual distribution modelapplicable to an actual distribution of the mobile devices based oncorrelating mobile device data, of mobile devices, to a definedparameter. The machine-readable storage medium can perform theoperations comprising generating a machine-learned distribution model ofthe mobile devices based on the actual distribution model. Based on theactual distribution model and the machine-learned distribution model,the machine-readable storage medium can perform the operationscomprising generating an estimated distribution model of the mobiledevices. Additionally, in response to the generating the estimateddistribution model, the machine-readable storage medium can perform theoperations comprising obtaining a beam sweeping control parameter.

These and other embodiments or implementations are described in moredetail below with reference to the drawings.

Referring now to FIG. 1, illustrated is an example wirelesscommunication system 100 in accordance with various aspects andembodiments of the subject disclosure. In one or more embodiments,system 100 can comprise one or more user equipment UEs 102. Thenon-limiting term user equipment can refer to any type of device thatcan communicate with a network node in a cellular or mobilecommunication system. A UE can have one or more antenna panels havingvertical and horizontal elements. Examples of a UE comprise a targetdevice, device to device (D2D) UE, machine type UE or UE capable ofmachine to machine (M2M) communications, personal digital assistant(PDA), tablet, mobile terminals, smart phone, laptop mounted equipment(LME), universal serial bus (USB) dongles enabled for mobilecommunications, a computer having mobile capabilities, a mobile devicesuch as cellular phone, a laptop having laptop embedded equipment (LEE,such as a mobile broadband adapter), a tablet computer having a mobilebroadband adapter, a wearable device, a virtual reality (VR) device, aheads-up display (HUD) device, a smart car, a machine-type communication(MTC) device, and the like. User equipment UE 102 can also comprise IOTdevices that communicate wirelessly.

In various embodiments, system 100 is or comprises a wirelesscommunication network serviced by one or more wireless communicationnetwork providers. In example embodiments, a UE 102 can becommunicatively coupled to the wireless communication network via anetwork node 104. The network node (e.g., network node device) cancommunicate with user equipment (UE), thus providing connectivitybetween the UE and the wider cellular network. The UE 102 can sendtransmission type recommendation data to the network node 104. Thetransmission type recommendation data can comprise a recommendation totransmit data via a closed loop MIMO mode and/or a rank-1 precoder mode.

A network node can have a cabinet and other protected enclosures, anantenna mast, and multiple antennas for performing various transmissionoperations (e.g., MIMO operations). Network nodes can serve severalcells, also called sectors, depending on the configuration and type ofantenna. In example embodiments, the UE 102 can send and/or receivecommunication data via a wireless link to the network node 104. Thedashed arrow lines from the network node 104 to the UE 102 representdownlink (DL) communications and the solid arrow lines from the UE 102to the network nodes 104 represents an uplink (UL) communication.

System 100 can further include one or more communication serviceprovider networks 106 that facilitate providing wireless communicationservices to various UEs, including UE 102, via the network node 104and/or various additional network devices (not shown) included in theone or more communication service provider networks 106. The one or morecommunication service provider networks 106 can include various types ofdisparate networks, including but not limited to: cellular networks,femto networks, picocell networks, microcell networks, internet protocol(IP) networks Wi-Fi service networks, broadband service network,enterprise networks, cloud based networks, and the like. For example, inat least one implementation, system 100 can be or include a large scalewireless communication network that spans various geographic areas.According to this implementation, the one or more communication serviceprovider networks 106 can be or include the wireless communicationnetwork and/or various additional devices and components of the wirelesscommunication network (e.g., additional network devices and cell,additional UEs, network server devices, etc.). The network node 104 canbe connected to the one or more communication service provider networks106 via one or more backhaul links 108. For example, the one or morebackhaul links 108 can comprise wired link components, such as a T1/E1phone line, a digital subscriber line (DSL) (e.g., either synchronous orasynchronous), an asymmetric DSL (ADSL), an optical fiber backbone, acoaxial cable, and the like. The one or more backhaul links 108 can alsoinclude wireless link components, such as but not limited to,line-of-sight (LOS) or non-LOS links which can include terrestrialair-interfaces or deep space links (e.g., satellite communication linksfor navigation).

Wireless communication system 100 can employ various cellular systems,technologies, and modulation modes to facilitate wireless radiocommunications between devices (e.g., the UE 102 and the network node104). While example embodiments might be described for 5G new radio (NR)systems, the embodiments can be applicable to any radio accesstechnology (RAT) or multi-RAT system where the UE operates usingmultiple carriers e.g. LTE FDD/TDD, GSM/GERAN, CDMA2000 etc.

For example, system 100 can operate in accordance with global system formobile communications (GSM), universal mobile telecommunications service(UMTS), long term evolution (LTE), LTE frequency division duplexing (LTEFDD, LTE time division duplexing (TDD), high speed packet access (HSPA),code division multiple access (CDMA), wideband CDMA (WCMDA), CDMA2000,time division multiple access (TDMA), frequency division multiple access(FDMA), multi-carrier code division multiple access (MC-CDMA),single-carrier code division multiple access (SC-CDMA), single-carrierFDMA (SC-FDMA), orthogonal frequency division multiplexing (OFDM),discrete Fourier transform spread OFDM (DFT-spread OFDM) single carrierFDMA (SC-FDMA), Filter bank based multi-carrier (FBMC), zero tailDFT-spread-OFDM (ZT DFT-s-OFDM), generalized frequency divisionmultiplexing (GFDM), fixed mobile convergence (FMC), universal fixedmobile convergence (UFMC), unique word OFDM (UW-OFDM), unique wordDFT-spread OFDM (UW DFT-Spread-OFDM), cyclic prefix OFDM CP-OFDM,resource-block-filtered OFDM, Wi Fi, WLAN, WiMax, and the like. However,various features and functionalities of system 100 are particularlydescribed wherein the devices (e.g., the UEs 102 and the network node104) of system 100 are configured to communicate wireless signals usingone or more multi carrier modulation schemes, wherein data symbols canbe transmitted simultaneously over multiple frequency subcarriers (e.g.,OFDM, CP-OFDM, DFT-spread OFMD, UFMC, FMBC, etc.). The embodiments areapplicable to single carrier as well as to multicarrier (MC) or carrieraggregation (CA) operation of the UE. The term carrier aggregation (CA)is also called (e.g. interchangeably called) “multi-carrier system”,“multi-cell operation”, “multi-carrier operation”, “multi-carrier”transmission and/or reception. Note that some embodiments are alsoapplicable for Multi RAB (radio bearers) on some carriers (that is dataplus speech is simultaneously scheduled).

In various embodiments, system 100 can be configured to provide andemploy 5G wireless networking features and functionalities. 5G wirelesscommunication networks are expected to fulfill the demand ofexponentially increasing data traffic and to allow people and machinesto enjoy gigabit data rates with virtually zero latency. Compared to 4G,5G supports more diverse traffic scenarios. For example, in addition tothe various types of data communication between conventional UEs (e.g.,phones, smartphones, tablets, PCs, televisions, Internet enabledtelevisions, etc.) supported by 4G networks, 5G networks can be employedto support data communication between smart cars in association withdriverless car environments, as well as machine type communications(MTCs). Considering the drastic different communication needs of thesedifferent traffic scenarios, the ability to dynamically configurewaveform parameters based on traffic scenarios while retaining thebenefits of multi carrier modulation schemes (e.g., OFDM and relatedschemes) can provide a significant contribution to the highspeed/capacity and low latency demands of 5G networks. With waveformsthat split the bandwidth into several sub-bands, different types ofservices can be accommodated in different sub-bands with the mostsuitable waveform and numerology, leading to an improved spectrumutilization for 5G networks.

To meet the demand for data centric applications, features of proposed5G networks may comprise: increased peak bit rate (e.g., 20 Gbps),larger data volume per unit area (e.g., high system spectralefficiency—for example about 3.5 times that of spectral efficiency oflong term evolution (LTE) systems), high capacity that allows moredevice connectivity both concurrently and instantaneously, lowerbattery/power consumption (which reduces energy and consumption costs),better connectivity regardless of the geographic region in which a useris located, a larger numbers of devices, lower infrastructuraldevelopment costs, and higher reliability of the communications. Thus,5G networks may allow for: data rates of several tens of megabits persecond should be supported for tens of thousands of users, 1 gigabit persecond to be offered simultaneously to tens of workers on the sameoffice floor, for example; several hundreds of thousands of simultaneousconnections to be supported for massive sensor deployments; improvedcoverage, enhanced signaling efficiency; reduced latency compared toLTE.

The upcoming 5G access network may utilize higher frequencies (e.g., >6GHz) to aid in increasing capacity. Currently, much of the millimeterwave (mmWave) spectrum, the band of spectrum between 30 gigahertz (Ghz)and 300 Ghz is underutilized. The millimeter waves have shorterwavelengths that range from 10 millimeters to 1 millimeter, and thesemmWave signals experience severe path loss, penetration loss, andfading. However, the shorter wavelength at mmWave frequencies alsoallows more antennas to be packed in the same physical dimension, whichallows for large-scale spatial multiplexing and highly directionalbeamforming.

Performance can be improved if both the transmitter and the receiver areequipped with multiple antennas. Multi-antenna techniques cansignificantly increase the data rates and reliability of a wirelesscommunication system. The use of multiple input multiple output (MIMO)techniques, which was introduced in the third-generation partnershipproject (3GPP) and has been in use (including with LTE), is amulti-antenna technique that can improve the spectral efficiency oftransmissions, thereby significantly boosting the overall data carryingcapacity of wireless systems. The use of multiple-input multiple-output(MIMO) techniques can improve mmWave communications, and has been widelyrecognized a potentially important component for access networksoperating in higher frequencies. MIMO can be used for achievingdiversity gain, spatial multiplexing gain and beamforming gain. Forthese reasons, MIMO systems are an important part of the 3rd and 4thgeneration wireless systems, and are planned for use in 5G systems.

Referring now to FIG. 2, illustrated is an example schematic systemblock diagram of radio access network intelligent controller 200 foradaptive beam sweeping according to one or more embodiments.

In the embodiment shown in FIG. 2, the RIC 200 can comprisesub-components (e.g., context correlator component 202, distributionmodel estimator component 204, AI component 208, and beam sweepingcontrol estimator component 206), processor 210 and memory 212 canbi-directionally communicate with each other. It should also be notedthat in alternative embodiments that other components including, but notlimited to the sub-components, processor 210, and/or memory 212, can beexternal to the RIC 200. Aspects of the processor 210 can constitutemachine-executable component(s) embodied within machine(s), e.g.,embodied in one or more computer readable mediums (or media) associatedwith one or more machines. Such component(s), when executed by the oneor more machines, e.g., computer(s), computing device(s), virtualmachine(s), etc. can cause the machine(s) to perform the operationsdescribed by the RIC 200. In an aspect, the RIC 200 can also includememory 212 that stores computer executable components and instructions.

The RIC 200 can receive data from a history based model that representshistorical events within the wireless network. This data can then beused ahead of time to direct a beam sweeping pattern. However, inreal-time, the UE distribution can be dynamic but can also be consideredby the RIC 200. For example, if a concert is occurring, which did notoccur before, then the historical model would not account for this.However, a real-time model can account for the concert and therefore becombined with the historical model to generate a more holistic UEdistribution model to facilitate adaptive beam sweeping. The UE contextmodel can be defined ahead of time and be a static model based onknowledge of how certain UE distributions look with respect to specificevents. The UE context correlator component 202, can receive historicalUE distribution model data. A real-time UE model can then be shared withthe UE distribution model estimator component 204 to estimate a new UEdistribution model. The UE distribution model estimator component 204can also receive an ML model from the AI component 208 based on thehistorical data. The UE distribution model estimator component 204 canthen send the new UE distribution model (based on the real-time UE modeland the ML model) to the beam sweeping control estimator component 206,which, in turn, can estimate beam-sweeping patterns for scanningperiodicity and/or width.

Referring now to FIG. 3, illustrated is an example schematic systemblock diagram of UE distributed model as a multi-dimensional contextspace system 300 for adaptive beam sweeping according to one or moreembodiments. Repetitive description of like elements employed in otherembodiments described herein is omitted for sake of brevity.

The multi-dimensional context space system 300 can comprise the UEdistribution model estimator component 204, which can receive contextdata based on contextual network information associated with the UEs102. For example, one dimension can be associated with time 302 (e.g.,time of day, morning commute time, lunch hour, evening commute hour, dayof the week, holiday, etc.) as it relates to UE 102 distributions. Asecond dimension can be associated with location 304 (e.g., highway,office building, hotel, convention center, airport, etc.) as it relatesto UE 102 distributions. Additionally, a third dimension can beassociated with an event 306 (e.g., concert, football game, ThanksgivingMacy's Parade, emergency response, etc.) as it relates to UE 102distributions. These dimensions can be used to sectorize real-time UEmeasurement data as provided by the O-RAN as later discussed withregards to FIG. 5.

Referring now to FIG. 4 and FIG. 5, illustrated is an example schematicsystem comprising a block diagram of radio access network intelligentcontroller, an external machine-learning component, and an O-RAN foradaptive beam sweeping according to one or more embodiments. Repetitivedescription of like elements employed in other embodiments describedherein is omitted for sake of brevity.

In an alternative embodiment, with regards to FIG. 4, a system 400 cancomprise an AI training component 402 can exist external to the RIC 200.Once the UE 102 data is collected with the different contexts, the AItraining component 402 can complete the correlation between thehistorical data and real-time UE data and generate a model. The AItraining component 402 can then send an AI based model to the AIcomponent 208.

With regards to FIG. 5, a system 500 can comprise the UE contextcorrelator component 202, can receive historical UE distribution modeldata and receive real-time UE distribution model data from the O-RAN502. The UE context correlator component 202 can then take data from theO-RAN and partition it into different slots (e.g., time, location,event) of the UE context model. The real-time UE model can then beshared with the UE distribution model estimator component 204 toestimate a new UE distribution model. The UE distribution modelestimator component 204 can then send the new UE distribution model tothe beam sweeping control estimator component 206, which, in turn, canestimate beam-sweeping patterns for scanning periodicity and/or width.The beam sweeping control estimator component 206 can then send beamsweeping control parameters associated with the estimated beam sweepingpatterns back to the O-RAN 502.

Referring now to FIG. 6, illustrated is an example flow diagram for amethod for adaptive beam sweeping for a 5G network according to one ormore embodiments.

At element 600, a method can comprise modeling (via the RIC 200) areal-time distribution of the mobile devices (e.g., UE 102) based onattribute data representative of attributes of mobile devices (e.g., UE102), resulting in a real-time model. At element 602, the method cancomprise correlating (via the context correlator component 202) theattribute data to defined parameters to assign a context to theattributes. Based on a result of the correlating, at element 604, themethod can comprise facilitating generating a machine-learning model(via the AI component 208). Additionally, at element 606, based on themachine-learning model, the method can comprise estimating adistribution model (via the distribution model estimator component 204),resulting in an estimated distribution model used to determine a beamsweep process to be applied to the mobile devices.

Referring now to FIG. 7, illustrated is an example flow diagram for asystem for adaptive beam sweeping for a 5G network according to one ormore embodiments.

At element 700, a system can facilitate, the modeling a real-timedistribution of the mobile devices (e.g., UE 102) based on attributedata representative of attributes of mobile devices (e.g., UE 102),resulting in a real-time distribution model. At element 702, the systemcan comprise correlating (via the context correlator component 202) theattribute data to defined parameters to assign a context to theattributes. In response to the correlating and based on historicaldistribution data, at element 704, the system can comprise generating amachine-learned distribution model (via the AI component 208). Based onthe real-time distribution model and the machine-learned distributionmodel, at element 706, the system can comprise estimating a distributionmodel (via the distribution model estimator component 204), resulting inan estimated distribution model applicable to the mobile devices (e.g.,UE 102). Additionally, based on the estimated distribution model, atelement 708, the system can comprise determining a beam sweeping controlparameter (via the beam sweeping control estimator component 206).

Referring now to FIG. 8, illustrated is an example flow diagram for amachine-readable medium for adaptive beam sweeping for a 5G networkaccording to one or more embodiments.

At element 800, a machine-readable storage medium that can perform theoperations comprising generating a distribution model of the mobiledevices, wherein the distribution model is an actual distribution modelapplicable to an actual distribution of the mobile devices (e.g., UE102) based on correlating mobile device data (via the context correlatorcomponent 202), of mobile devices (e.g., UE 102), to a definedparameter. At element 802, the machine-readable storage medium canperform the operations comprising generating a machine-learneddistribution model (via the AI component 208) of the mobile devicesbased on the actual distribution model. Based on the actual distributionmodel and the machine-learned distribution model, at element 804, themachine-readable storage medium can perform the operations comprisinggenerating (via the distribution model estimator component 204) anestimated distribution model of the mobile devices. Additionally, atelement 806, in response to the generating the estimated distributionmodel, the machine-readable storage medium can perform the operationscomprising obtaining a beam sweeping control parameter (via the beamsweeping control estimator component 206).

Referring now to FIG. 9, illustrated is an example block diagram of anexample mobile handset 900 operable to engage in a system architecturethat facilitates wireless communications according to one or moreembodiments described herein. Although a mobile handset is illustratedherein, it will be understood that other devices can be a mobile device,and that the mobile handset is merely illustrated to provide context forthe embodiments of the various embodiments described herein. Thefollowing discussion is intended to provide a brief, general descriptionof an example of a suitable environment in which the various embodimentscan be implemented. While the description includes a general context ofcomputer-executable instructions embodied on a machine-readable storagemedium, those skilled in the art will recognize that the innovation alsocan be implemented in combination with other program modules and/or as acombination of hardware and software.

Generally, applications (e.g., program modules) can include routines,programs, components, data structures, etc., that perform particulartasks or implement particular abstract data types. Moreover, thoseskilled in the art will appreciate that the methods described herein canbe practiced with other system configurations, includingsingle-processor or multiprocessor systems, minicomputers, mainframecomputers, as well as personal computers, hand-held computing devices,microprocessor-based or programmable consumer electronics, and the like,each of which can be operatively coupled to one or more associateddevices.

A computing device can typically include a variety of machine-readablemedia. Machine-readable media can be any available media that can beaccessed by the computer and includes both volatile and non-volatilemedia, removable and non-removable media. By way of example and notlimitation, computer-readable media can comprise computer storage mediaand communication media. Computer storage media can include volatileand/or non-volatile media, removable and/or non-removable mediaimplemented in any method or technology for storage of information, suchas computer-readable instructions, data structures, program modules, orother data. Computer storage media can include, but is not limited to,RAM, ROM, EEPROM, flash memory or other memory technology, CD ROM,digital video disk (DVD) or other optical disk storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store thedesired information and which can be accessed by the computer.

Communication media typically embodies computer-readable instructions,data structures, program modules, or other data in a modulated datasignal such as a carrier wave or other transport mechanism, and includesany information delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared and other wireless media. Combinations of the anyof the above should also be included within the scope ofcomputer-readable media.

The handset includes a processor 902 for controlling and processing allonboard operations and functions. A memory 904 interfaces to theprocessor 902 for storage of data and one or more applications 906(e.g., a video player software, user feedback component software, etc.).Other applications can include voice recognition of predetermined voicecommands that facilitate initiation of the user feedback signals. Theapplications 906 can be stored in the memory 904 and/or in a firmware908, and executed by the processor 902 from either or both the memory904 or/and the firmware 908. The firmware 908 can also store startupcode for execution in initializing the handset 900. A communicationscomponent 910 interfaces to the processor 902 to facilitatewired/wireless communication with external systems, e.g., cellularnetworks, VoIP networks, and so on. Here, the communications component910 can also include a suitable cellular transceiver 911 (e.g., a GSMtransceiver) and/or an unlicensed transceiver 913 (e.g., Wi-Fi, WiMax)for corresponding signal communications. The handset 900 can be a devicesuch as a cellular telephone, a PDA with mobile communicationscapabilities, and messaging-centric devices. The communicationscomponent 910 also facilitates communications reception from terrestrialradio networks (e.g., broadcast), digital satellite radio networks, andInternet-based radio services networks.

The handset 900 includes a display 912 for displaying text, images,video, telephony functions (e.g., a Caller ID function), setupfunctions, and for user input. For example, the display 912 can also bereferred to as a “screen” that can accommodate the presentation ofmultimedia content (e.g., music metadata, messages, wallpaper, graphics,etc.). The display 912 can also display videos and can facilitate thegeneration, editing and sharing of video quotes. A serial I/O interface914 is provided in communication with the processor 902 to facilitatewired and/or wireless serial communications (e.g., USB, and/or IEEE1394) through a hardwire connection, and other serial input devices(e.g., a keyboard, keypad, and mouse). This can support updating andtroubleshooting the handset 900, for example. Audio capabilities areprovided with an audio I/O component 916, which can include a speakerfor the output of audio signals related to, for example, indication thatthe user pressed the proper key or key combination to initiate the userfeedback signal. The audio I/O component 916 also facilitates the inputof audio signals through a microphone to record data and/or telephonyvoice data, and for inputting voice signals for telephone conversations.

The handset 900 can include a slot interface 918 for accommodating a SIC(Subscriber Identity Component) in the form factor of a card SubscriberIdentity Module (SIM) or universal SIM 920, and interfacing the SIM card920 with the processor 902. However, it is to be appreciated that theSIM card 920 can be manufactured into the handset 900, and updated bydownloading data and software.

The handset 900 can process IP data traffic through the communicationscomponent 910 to accommodate IP traffic from an IP network such as, forexample, the Internet, a corporate intranet, a home network, a personarea network, etc., through an ISP or broadband cable provider. Thus,VoIP traffic can be utilized by the handset 900 and IP-based multimediacontent can be received in either an encoded or decoded format.

A video processing component 922 (e.g., a camera) can be provided fordecoding encoded multimedia content. The video processing component 922can aid in facilitating the generation, editing, and sharing of videoquotes. The handset 900 also includes a power source 924 in the form ofbatteries and/or an AC power subsystem, which power source 924 caninterface to an external power system or charging equipment (not shown)by a power I/O component 926.

The handset 900 can also include a video component 930 for processingvideo content received and, for recording and transmitting videocontent. For example, the video component 930 can facilitate thegeneration, editing and sharing of video quotes. A location trackingcomponent 932 facilitates geographically locating the handset 900. Asdescribed hereinabove, this can occur when the user initiates thefeedback signal automatically or manually. A user input component 934facilitates the user initiating the quality feedback signal. The userinput component 934 can also facilitate the generation, editing andsharing of video quotes. The user input component 934 can include suchconventional input device technologies such as a keypad, keyboard,mouse, stylus pen, and/or touchscreen, for example.

Referring again to the applications 906, a hysteresis component 936facilitates the analysis and processing of hysteresis data, which isutilized to determine when to associate with the access point. Asoftware trigger component 938 can be provided that facilitatestriggering of the hysteresis component 936 when the Wi-Fi transceiver913 detects the beacon of the access point. A SIP client 940 enables thehandset 900 to support SIP protocols and register the subscriber withthe SIP registrar server. The applications 906 can also include a client942 that provides at least the capability of discovery, play and storeof multimedia content, for example, music.

The handset 900, as indicated above related to the communicationscomponent 910, includes an indoor network radio transceiver 913 (e.g.,Wi-Fi transceiver). This function supports the indoor radio link, suchas IEEE 802.11, for the dual-mode GSM handset 900. The handset 900 canaccommodate at least satellite radio services through a handset that cancombine wireless voice and digital radio chipsets into a single handhelddevice.

Referring now to FIG. 10, illustrated is an example block diagram of anexample computer 1000 operable to engage in a system architecture thatfacilitates wireless communications according to one or more embodimentsdescribed herein. The computer 1000 can provide networking andcommunication capabilities between a wired or wireless communicationnetwork and a server (e.g., Microsoft server) and/or communicationdevice. In order to provide additional context for various aspectsthereof, FIG. 10 and the following discussion are intended to provide abrief, general description of a suitable computing environment in whichthe various aspects of the innovation can be implemented to facilitatethe establishment of a transaction between an entity and a third party.While the description above is in the general context ofcomputer-executable instructions that can run on one or more computers,those skilled in the art will recognize that the innovation also can beimplemented in combination with other program modules and/or as acombination of hardware and software.

Generally, program modules include routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the inventive methods can be practiced with other computer systemconfigurations, including single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

The illustrated aspects of the innovation can also be practiced indistributed computing environments where certain tasks are performed byremote processing devices that are linked through a communicationsnetwork. In a distributed computing environment, program modules can belocated in both local and remote memory storage devices.

Computing devices typically include a variety of media, which caninclude computer-readable storage media or communications media, whichtwo terms are used herein differently from one another as follows.

Computer-readable storage media can be any available storage media thatcan be accessed by the computer and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structureddata, or unstructured data. Computer-readable storage media can include,but are not limited to, RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disk (DVD) or other optical diskstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or other tangible and/or non-transitorymedia which can be used to store desired information. Computer-readablestorage media can be accessed by one or more local or remote computingdevices, e.g., via access requests, queries or other data retrievalprotocols, for a variety of operations with respect to the informationstored by the medium.

Communications media can embody computer-readable instructions, datastructures, program modules or other structured or unstructured data ina data signal such as a modulated data signal, e.g., a carrier wave orother transport mechanism, and includes any information delivery ortransport media. The term “modulated data signal” or signals refers to asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in one or more signals. By way ofexample, and not limitation, communication media include wired media,such as a wired network or direct-wired connection, and wireless mediasuch as acoustic, RF, infrared and other wireless media.

With reference to FIG. 10, implementing various aspects described hereinwith regards to the end-user device can include a computer 1000, thecomputer 1000 including a processing unit 1004, a system memory 1006 anda system bus 1008. The system bus 1008 couples system componentsincluding, but not limited to, the system memory 1006 to the processingunit 1004. The processing unit 1004 can be any of various commerciallyavailable processors. Dual microprocessors and other multi-processorarchitectures can also be employed as the processing unit 1004.

The system bus 1008 can be any of several types of bus structure thatcan further interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 1006includes read-only memory (ROM) 1027 and random access memory (RAM)1012. A basic input/output system (BIOS) is stored in a non-volatilememory 1027 such as ROM, EPROM, EEPROM, which BIOS contains the basicroutines that help to transfer information between elements within thecomputer 1000, such as during start-up. The RAM 1012 can also include ahigh-speed RAM such as static RAM for caching data.

The computer 1000 further includes an internal hard disk drive (HDD)1014 (e.g., EIDE, SATA), which internal hard disk drive 1014 can also beconfigured for external use in a suitable chassis (not shown), amagnetic floppy disk drive (FDD) 1016, (e.g., to read from or write to aremovable diskette 1018) and an optical disk drive 1020, (e.g., readinga CD-ROM disk 1022 or, to read from or write to other high capacityoptical media such as the DVD). The hard disk drive 1014, magnetic diskdrive 1016 and optical disk drive 1020 can be connected to the systembus 1008 by a hard disk drive interface 1024, a magnetic disk driveinterface 1026 and an optical drive interface 1028, respectively. Theinterface 1024 for external drive implementations includes at least oneor both of Universal Serial Bus (USB) and IEEE 1394 interfacetechnologies. Other external drive connection technologies are withincontemplation of the subject innovation.

The drives and their associated computer-readable media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 1000 the drives and mediaaccommodate the storage of any data in a suitable digital format.Although the description of computer-readable media above refers to aHDD, a removable magnetic diskette, and a removable optical media suchas a CD or DVD, it should be appreciated by those skilled in the artthat other types of media which are readable by a computer 1000, such aszip drives, magnetic cassettes, flash memory cards, cartridges, and thelike, can also be used in the exemplary operating environment, andfurther, that any such media can contain computer-executableinstructions for performing the methods of the disclosed innovation.

A number of program modules can be stored in the drives and RAM 1012,including an operating system 1030, one or more application programs1032, other program modules 1034 and program data 1036. All or portionsof the operating system, applications, modules, and/or data can also becached in the RAM 1012. It is to be appreciated that the innovation canbe implemented with various commercially available operating systems orcombinations of operating systems.

A user can enter commands and information into the computer 1000 throughone or more wired/wireless input devices, e.g., a keyboard 1038 and apointing device, such as a mouse 1040. Other input devices (not shown)can include a microphone, an IR remote control, a joystick, a game pad,a stylus pen, touchscreen, or the like. These and other input devicesare often connected to the processing unit 1004 through an input deviceinterface 1042 that is coupled to the system bus 1008, but can beconnected by other interfaces, such as a parallel port, an IEEE 1394serial port, a game port, a USB port, an IR interface, etc.

A monitor 1044 or other type of display device is also connected to thesystem bus 1008 through an interface, such as a video adapter 1046. Inaddition to the monitor 1044, a computer 1000 typically includes otherperipheral output devices (not shown), such as speakers, printers, etc.

The computer 1000 can operate in a networked environment using logicalconnections by wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 1048. The remotecomputer(s) 1048 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentdevice, a peer device or other common network node, and typicallyincludes many or all of the elements described relative to the computer,although, for purposes of brevity, only a memory/storage device 1050 isillustrated. The logical connections depicted include wired/wirelessconnectivity to a local area network (LAN) 1052 and/or larger networks,e.g., a wide area network (WAN) 1054. Such LAN and WAN networkingenvironments are commonplace in offices and companies, and facilitateenterprise-wide computer networks, such as intranets, all of which canconnect to a global communications network, e.g., the Internet.

When used in a LAN networking environment, the computer 1000 isconnected to the local network 1052 through a wired and/or wirelesscommunication network interface or adapter 1056. The adapter 1056 canfacilitate wired or wireless communication to the LAN 1052, which canalso include a wireless access point disposed thereon for communicatingwith the wireless adapter 1056.

When used in a WAN networking environment, the computer 1000 can includea modem 1058, or is connected to a communications server on the WAN1054, or has other means for establishing communications over the WAN1054, such as by way of the Internet. The modem 1058, which can beinternal or external and a wired or wireless device, is connected to thesystem bus 1008 through the input device interface 1042. In a networkedenvironment, program modules depicted relative to the computer, orportions thereof, can be stored in the remote memory/storage device1050. It will be appreciated that the network connections shown areexemplary and other means of establishing a communications link betweenthe computers can be used.

The computer is operable to communicate with any wireless devices orentities operatively disposed in wireless communication, e.g., aprinter, scanner, desktop and/or portable computer, portable dataassistant, communications satellite, any piece of equipment or locationassociated with a wirelessly detectable tag (e.g., a kiosk, news stand,restroom), and telephone. This includes at least Wi-Fi and Bluetooth™wireless technologies. Thus, the communication can be a predefinedstructure as with a conventional network or simply an ad hoccommunication between at least two devices.

Wi-Fi, or Wireless Fidelity, allows connection to the Internet from acouch at home, in a hotel room, or a conference room at work, withoutwires. Wi-Fi is a wireless technology similar to that used in a cellphone that enables such devices, e.g., computers, to send and receivedata indoors and out; anywhere within the range of a base station. Wi-Finetworks use radio technologies called IEEE 802.11 (a, b, g, etc.) toprovide secure, reliable, fast wireless connectivity. A Wi-Fi networkcan be used to connect computers to each other, to the Internet, and towired networks (which use IEEE 802.3 or Ethernet). Wi-Fi networksoperate in the unlicensed 2.4 and 5 GHz radio bands, at an 7 Mbps(802.11a) or 54 Mbps (802.11b) data rate, for example, or with productsthat contain both bands (dual band), so the networks can providereal-world performance similar to the basic 16BaseT wired Ethernetnetworks used in many offices.

An aspect of 5G, which differentiates from previous 4G systems, is theuse of NR. NR architecture can be designed to support multipledeployment cases for independent configuration of resources used forRACH procedures. Since the NR can provide additional services than thoseprovided by LTE, efficiencies can be generated by leveraging the prosand cons of LTE and NR to facilitate the interplay between LTE and NR,as discussed herein.

Reference throughout this specification to “one embodiment,” or “anembodiment,” means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment. Thus, the appearances of the phrase “in oneembodiment,” “in one aspect,” or “in an embodiment,” in various placesthroughout this specification are not necessarily all referring to thesame embodiment. Furthermore, the particular features, structures, orcharacteristics can be combined in any suitable manner in one or moreembodiments.

As used in this disclosure, in some embodiments, the terms “component,”“system,” “interface,” and the like are intended to refer to, orcomprise, a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution, and/or firmware. As anexample, a component can be, but is not limited to being, a processrunning on a processor, a processor, an object, an executable, a threadof execution, computer-executable instructions, a program, and/or acomputer. By way of illustration and not limitation, both an applicationrunning on a server and the server can be a component.

One or more components can reside within a process and/or thread ofexecution and a component can be localized on one computer and/ordistributed between two or more computers. In addition, these componentscan execute from various computer readable media having various datastructures stored thereon. The components can communicate via localand/or remote processes such as in accordance with a signal having oneor more data packets (e.g., data from one component interacting withanother component in a local system, distributed system, and/or across anetwork such as the Internet with other systems via the signal). Asanother example, a component can be an apparatus with specificfunctionality provided by mechanical parts operated by electric orelectronic circuitry, which is operated by a software application orfirmware application executed by one or more processors, wherein theprocessor can be internal or external to the apparatus and can executeat least a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can comprise a processor therein to executesoftware or firmware that confer(s) at least in part the functionalityof the electronic components. In an aspect, a component can emulate anelectronic component via a virtual machine, e.g., within a cloudcomputing system. While various components have been illustrated asseparate components, it will be appreciated that multiple components canbe implemented as a single component, or a single component can beimplemented as multiple components, without departing from exampleembodiments.

In addition, the words “example” and “exemplary” are used herein to meanserving as an instance or illustration. Any embodiment or designdescribed herein as “example” or “exemplary” is not necessarily to beconstrued as preferred or advantageous over other embodiments ordesigns. Rather, use of the word example or exemplary is intended topresent concepts in a concrete fashion. As used in this application, theterm “or” is intended to mean an inclusive “or” rather than an exclusive“or.” That is, unless specified otherwise or clear from context, “Xemploys A or B” is intended to mean any of the natural inclusivepermutations. That is, if X employs A; X employs B; or X employs both Aand B, then “X employs A or B” is satisfied under any of the foregoinginstances. In addition, the articles “a” and “an” as used in thisapplication and the appended claims should generally be construed tomean “one or more” unless specified otherwise or clear from context tobe directed to a singular form.

Moreover, terms such as “mobile device equipment,” “mobile station,”“mobile,” subscriber station,” “access terminal,” “terminal,” “handset,”“communication device,” “mobile device” (and/or terms representingsimilar terminology) can refer to a wireless device utilized by asubscriber or mobile device of a wireless communication service toreceive or convey data, control, voice, video, sound, gaming orsubstantially any data-stream or signaling-stream. The foregoing termsare utilized interchangeably herein and with reference to the relateddrawings. Likewise, the terms “access point (AP),” “Base Station (BS),”BS transceiver, BS device, cell site, cell site device, “Node B (NB),”“evolved Node B (eNode B),” “home Node B (HNB)” and the like, areutilized interchangeably in the application, and refer to a wirelessnetwork component or appliance that transmits and/or receives data,control, voice, video, sound, gaming or substantially any data-stream orsignaling-stream from one or more subscriber stations. Data andsignaling streams can be packetized or frame-based flows.

Furthermore, the terms “device,” “communication device,” “mobiledevice,” “subscriber,” “customer entity,” “consumer,” “customer entity,”“entity” and the like are employed interchangeably throughout, unlesscontext warrants particular distinctions among the terms. It should beappreciated that such terms can refer to human entities or automatedcomponents supported through artificial intelligence (e.g., a capacityto make inference based on complex mathematical formalisms), which canprovide simulated vision, sound recognition and so forth.

Embodiments described herein can be exploited in substantially anywireless communication technology, comprising, but not limited to,wireless fidelity (Wi-Fi), global system for mobile communications(GSM), universal mobile telecommunications system (UMTS), worldwideinteroperability for microwave access (WiMAX), enhanced general packetradio service (enhanced GPRS), third generation partnership project(3GPP) long term evolution (LTE), third generation partnership project 2(3GPP2) ultra mobile broadband (UMB), high speed packet access (HSPA),Z-Wave, Zigbee and other 802.XX wireless technologies and/or legacytelecommunication technologies.

The various aspects described herein can relate to New Radio (NR), whichcan be deployed as a standalone radio access technology or as anon-standalone radio access technology assisted by another radio accesstechnology, such as Long Term Evolution (LTE), for example. It should benoted that although various aspects and embodiments have been describedherein in the context of 5G, Universal Mobile Telecommunications System(UMTS), and/or Long Term Evolution (LTE), or other next generationnetworks, the disclosed aspects are not limited to 5G, a UMTSimplementation, and/or an LTE implementation as the techniques can alsobe applied in 3G, 4G, or LTE systems. For example, aspects or featuresof the disclosed embodiments can be exploited in substantially anywireless communication technology. Such wireless communicationtechnologies can include UMTS, Code Division Multiple Access (CDMA),Wi-Fi, Worldwide Interoperability for Microwave Access (WiMAX), GeneralPacket Radio Service (GPRS), Enhanced GPRS, Third Generation PartnershipProject (3GPP), LTE, Third Generation Partnership Project 2 (3GPP2)Ultra Mobile Broadband (UMB), High Speed Packet Access (HSPA), EvolvedHigh Speed Packet Access (HSPA+), High-Speed Downlink Packet Access(HSDPA), High-Speed Uplink Packet Access (HSUPA), Zigbee, or anotherIEEE 802.XX technology. Additionally, substantially all aspectsdisclosed herein can be exploited in legacy telecommunicationtechnologies.

As used herein, the term “infer” or “inference” refers generally to theprocess of reasoning about, or inferring states of, the system,environment, user, and/or intent from a set of observations as capturedvia events and/or data. Captured data and events can include user data,device data, environment data, data from sensors, sensor data,application data, implicit data, explicit data, etc. Inference can beemployed to identify a specific context or action, or can generate aprobability distribution over states of interest based on aconsideration of data and events, for example.

Inference can also refer to techniques employed for composinghigher-level events from a set of events and/or data. Such inferenceresults in the construction of new events or actions from a set ofobserved events and/or stored event data, whether the events arecorrelated in close temporal proximity, and whether the events and datacome from one or several event and data sources. Various classificationprocedures and/or systems (e.g., support vector machines, neuralnetworks, expert systems, Bayesian belief networks, fuzzy logic, anddata fusion engines) can be employed in connection with performingautomatic and/or inferred action in connection with the disclosedsubject matter.

In addition, the various embodiments can be implemented as a method,apparatus, or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device, machine-readable device, computer-readablecarrier, computer-readable media, machine-readable media,computer-readable (or machine-readable) storage/communication media. Forexample, computer-readable media can comprise, but are not limited to, amagnetic storage device, e.g., hard disk; floppy disk; magneticstrip(s); an optical disk (e.g., compact disk (CD), a digital video disc(DVD), a Blu-ray Disc™ (BD)); a smart card; a flash memory device (e.g.,card, stick, key drive); and/or a virtual device that emulates a storagedevice and/or any of the above computer-readable media. Of course, thoseskilled in the art will recognize many modifications can be made to thisconfiguration without departing from the scope or spirit of the variousembodiments.

The above description of illustrated embodiments of the subjectdisclosure, including what is described in the Abstract, is not intendedto be exhaustive or to limit the disclosed embodiments to the preciseforms disclosed. While specific embodiments and examples are describedherein for illustrative purposes, various modifications are possiblethat are considered within the scope of such embodiments and examples,as those skilled in the relevant art can recognize.

In this regard, while the subject matter has been described herein inconnection with various embodiments and corresponding figures, whereapplicable, it is to be understood that other similar embodiments can beused or modifications and additions can be made to the describedembodiments for performing the same, similar, alternative, or substitutefunction of the disclosed subject matter without deviating therefrom.Therefore, the disclosed subject matter should not be limited to anysingle embodiment described herein, but rather should be construed inbreadth and scope in accordance with the appended claims below.

What is claimed is:
 1. A method, comprising: based on attribute datarepresentative of attributes of a first number of user equipment,modeling, by network equipment comprising a processor, a real-timedistribution of the first number of user equipment at a first time,resulting in a real-time model, wherein the attribute data comprises atime associated with utilization of the first number of user equipment alocation of the first number of user equipment, and a social eventassociated with the first number of user equipment; correlating, by thenetwork equipment, the attribute data to defined parameters, whereincorrelating the attribute data to the defined parameters comprisespartitioning the attribute data based on defined context attributescomprising a time context attribute, a location context attribute, and asocial event context attribute; based on a result of correlating theattribute data to the defined parameters, facilitating, by the networkequipment, generating a machine-learning model, wherein the machinelearning model is at least in part based on a historical user equipmentdistribution; based on the machine-learning model and the real-timemodel, estimating, by the network equipment, a distribution model,resulting in an estimated distribution model, to be used to determine abeam sweep process to be applied by the processor, at a second timelater than the first time, to a second number of user equipment, lessthan the first number of user equipment, that have previously utilizedthe beam sweep process; and based on the estimated distribution model,determining, by the network equipment, a beam sweep periodicity to beapplied to the beam sweep process.
 2. The method of claim 1, wherein theattribute data comprises a time associated with utilization of the firstnumber of user equipment.
 3. The method of claim 1, wherein the definedparameters comprise a commute time duration, and wherein correlating theattribute data comprises correlating the time associated with theutilization to the commute time.
 4. The method of claim 1, wherein theattribute data comprises a location associated with the utilization ofthe first number of user equipment.
 5. The method of claim 4, whereinthe defined parameters comprise an airport location, and whereincorrelating the attribute data comprises correlating the locationassociated with the utilization to the airport location.
 6. The methodof claim 1, wherein the attribute data comprises a concert eventassociated with the utilization of the first number of user equipment.7. The method of claim 6, wherein the defined parameters comprise anemergency event, and wherein correlating the attribute data comprisescorrelating the event associated with the utilization to the emergencyevent.
 8. A system, comprising: a processor; and a memory that storesexecutable instructions that, when executed by the processor, facilitateperformance of operations, comprising: based on attribute datarepresentative of attributes of a first number of user equipment,modeling a real-time distribution of the first number of user equipmentat a first time, resulting in a real-time distribution model, whereinthe attribute data comprises a time associated with utilization of thefirst number of user equipment a location of the first number of userequipment, and a social event associated with the first number of userequipment; correlating the attribute data to defined parameters, whereincorrelating the attribute data to the defined parameters comprisespartitioning the attribute data based on defined context attributescomprising a time context attribute, a location context attribute, and asocial event context attribute; in response to correlating the attributedata to the defined parameters and based on historical distributiondata, generating a machine-learned distribution model; based on thereal-time distribution model and the machine-learned distribution model,estimating a distribution model for a second time that is later than thefirst time, resulting in an estimated distribution model applicable to asecond number of user equipment, wherein the second number of userequipment is less than the first number of user equipment; and based onthe estimated distribution model, determining a beam sweeping controlparameter, wherein the beam sweeping control parameter comprises ascanning periodicity value to be applied, by the processor, to a beamsweep; and in response to the determining, applying the beam sweepingcontrol parameter to the second number of user equipment at the secondtime.
 9. The system of claim 8, wherein the operations further comprise:based on the beam sweeping control parameter, performing a beam sweep inaccordance with the estimated distribution model.
 10. The system ofclaim 9, wherein performing the beam sweep in accordance with theestimated distribution model reduces a power utilization of a basestation.
 11. The system of claim 8, wherein the real-time distributionmodel is based on a time, a hotel location, and a football eventassociated with the first number of user equipment.
 12. The system ofclaim 8, wherein the machine-learned distribution model is based on alunch hour time, an office building location, and a parade eventassociated with the first number of user equipment.
 13. The system ofclaim 8, wherein correlating the attribute data to the definedparameters comprises correlating a time of day to a commute timeassociated with a user of a user equipment of the first number of userequipment.
 14. The system of claim 8, wherein correlating the attributedata to the defined parameters comprises correlating a point-of-interestassociated with a user equipment of the first number of user equipmentto the location.
 15. A non-transitory machine-readable medium,comprising executable instructions that, when executed by a processor,facilitate performance of operations, comprising: based on correlatingmobile device data to a set of defined parameters, of a first number ofmobile devices at a first time, generating a current distribution modelof the first number of mobile devices, wherein the current distributionmodel comprises a time associated with utilization of the first numberof mobile devices, a location of the first number of mobile devices, anda social event associated with the first number of mobile devices, andwherein correlating the mobile device data to a set of definedparameters comprises partitioning attribute data based on a time contextattribute, a location context attribute, and a social event contextattribute; based on historical distribution model, generating amachine-learned distribution model of the first number of mobiledevices; based on the current distribution model and the machine-learneddistribution model, generating an estimated distribution model of asecond number of mobile devices less than the first number of devices;and in response to generating the estimated distribution model,obtaining a beam sweeping control parameter, wherein the beam sweepingcontrol parameter comprises a beam sweep periodicity to be applied, bythe processor, to a beam sweeping process; and in response to obtainingthe beam sweeping control parameter, applying the beam sweeping controlparameter to the second number of mobile devices at a second time laterthan the first time and based on the beam sweep periodicity.
 16. Thenon-transitory machine-readable medium of claim 15, wherein theoperations further comprise: utilizing the beam sweeping controlparameter to facilitate transmission of a beam to the first number ofmobile devices.
 17. The non-transitory machine-readable medium of claim15, wherein the mobile device data comprises usage data representativeof a previous usage pattern of the first number of mobile devices. 18.The non-transitory machine-readable medium of claim 15, wherein applyingthe beam sweep periodicity comprises a periodicity for transmission of abeam to a mobile device of the first number of mobile devices.
 19. Thenon-transitory machine-readable medium of claim 18, wherein theperiodicity is a first periodicity less than a second periodicityutilized prior to the obtaining the beam sweeping control parameter. 20.The non-transitory machine-readable medium of claim 15, wherein the beamsweeping control parameter comprises a beam width associated withtransmitting a beam to a mobile device of the first number of mobiledevices.